Results 231 to 240 of about 1,885,929 (310)

Arbuscular mycorrhizal fungi change root morphology and nutrient use efficiency in the tree legume Mimosa scabrella. [PDF]

Mycorrhiza
Primieri S, Costa MD, Rech TD, Ciotta MN, Stürmer SL.   +4 more
europepmc   +1 more source

Biomass Native Structure Into Functional Carbon‐Based Catalysts for Fenton‐Like Reactions

Advanced Functional Materials, EarlyView.
This study indicates that eight biomasses with 2D flaky and 1D acicular structures influence surface O types, morphology, defects, N doping, sp2 C, and Co nanoparticles loading in three series of carbon, N‐doped carbon, and cobalt/graphitic carbon. This work identifies how these structural factors impact catalytic pathways, enhancing selective electron
Wenjie Tian, Jingkai Lin, Zhihao Tian, Selusiwe Ncube, Huayang Zhang, Emiliano Cortés, Hongqi Sun, Shaobin Wang   +7 more
wiley   +1 more source

Soil Nutrient Availability By Beneficial Bacteria of Forest Trees: From Mechanisms To Applications. [PDF]

Microb Ecol
Wang Z, Wang Q, Liu Y, Du W, Hong L, Zhou D, Asiegbu FO, Wu P, Ma X, Wang K.   +9 more
europepmc   +1 more source

Composites of Shellac and Silver Nanowires as Flexible, Biobased, and Corrosion‐Resistant Transparent Conductive Electrodes

Advanced Functional Materials, EarlyView.
Shellac, a centuries‐old natural resin, is reimagined as a green material for flexible electronics. When combined with silver nanowires, shellac films deliver transparency, conductivity, and stability against humidity. These results position shellac as a sustainable alternative to synthetic polymers for transparent conductors in next‐generation ...
Rahaf Nafez Hussein, Tiago Carneiro Gomes, Eliza Ng, Simon Rondeau‐Gagné, Tricia Breen Carmichael   +4 more
wiley   +1 more source

Agreement Between an Artificial Intelligence-Based Meal Image Recognition System and the Weighed Dietary Record for Estimating Energy and Nutrient Intakes. [PDF]

Nutrients
Sunto A, Aizawa K, Yamakata Y, Iida A, Suzuki S.   +4 more
europepmc   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Global patterns and drivers of soil microbial nitrogen and phosphorus use efficiency. [PDF]

Nat Commun
Gao D, Kuzyakov Y, Delgado-Baquerizo M, Peñuelas J, Moorhead DL, Sinsabaugh RL, Xu X, Sun L, Wang H, Kou L, Fu X, Dai X, Meng S, Liu Z, Wang S, Hagedorn F, Rillig MC, Cui Y.   +17 more
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Seed coating with plant growth-promoting rhizobacteria enhances potato (<i>Solanum tuberosum</i> L.) growth and yield. [PDF]

Front Microbiol
Hu J, Xiang C, Lu Y, Jia M, Feng Z, Zhang S.   +5 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source